Domain-Dependent Safety Behavior in Open-Weight LLMs: An Empirical Study Across Seven Ethical Domains

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, medium

Summary

A systematic study by Zacharie Bugaud investigates domain-dependent safety behavior in open-weight Large Language Models (LLMs) across seven ethical domains. The research involved 7 standardized experiments, testing 5 models ranging from 12B to 70B parameters in 4,200 interactions, utilizing dual-judge validation. A dual-condition methodology was employed, examining scenarios in both an analytical framing (identifying harm) and an operational framing (assisting harm). Findings reveal significant variation in compliance rates, from 14.7% for human trafficking to 85.7% for surveillance design, a 71-percentage-point difference. Domain accounts for 36% of the variance in harm scores, surpassing scenario (26%) and model identity (15%). A consistent model safety hierarchy was observed across domains, with a mean Spearman ρ of 0.68. This indicates that safety alignment is not a general capability, and aggregate safety scores obscure critical domain-specific differences, necessitating targeted safety auditing.

Key takeaway

For AI Scientists and Trust & Safety teams deploying open-weight LLMs, relying solely on aggregate safety scores is insufficient. Your safety evaluations must incorporate domain-specific auditing. LLM compliance varies significantly across ethical domains, from 14.7% in human trafficking to 85.7% in surveillance design. This targeted approach ensures robust and trustworthy deployment by addressing specific risks relevant to your application's context.

Key insights

LLM safety alignment is domain-dependent, not a general capability, requiring specific auditing.

Principles

• LLM safety alignment is not a general capability.
• Aggregate safety scores mask critical domain-level variation.

Method

A dual-condition methodology tests scenarios in both analytical (identify harm) and operational (help commit harm) framings.

In practice

• Implement domain-specific safety auditing for LLM deployment.

Topics

• Open-Weight LLMs
• LLM Safety
• Ethical AI
• Domain-Specific Auditing
• Harm Assessment
• Trustworthy NLP

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.